pandas map using two columns with code examples

Pandas is a powerful data manipulation library that is widely used in the data science community. It provides a wide range of functions and tools to manipulate, analyze, and visualize data in various formats. One of the essential functions of Pandas is the "map" function. It helps us to transform data using a dictionary or a function. In this article, we will discuss how to use Pandas map with two columns using code examples.

Let's start by understanding the basics of the map function.

What is the map function?

The map function is a Pandas function that applies a function or a dictionary to each element of a Pandas series. The function or dictionary is applied to the elements based on the corresponding value of the index.

For example, let's create a Pandas series and apply a dictionary using the map function:

import pandas as pd

grades = pd.Series([90, 80, 70, 60, 50])
grades_dictionary = {90: "A", 80: "B", 70: "C", 60: "D", 50: "F"}

mapped_grades = grades.map(grades_dictionary)
print(mapped_grades)

Output:

0    A
1    B
2    C
3    D
4    F
dtype: object

In the above example, the dictionary "grades_dictionary" is applied to the series "grades", which results in a new series "mapped_grades" with the letter grades.

Now, let's discuss how we can use the Pandas map function with two columns.

Using Pandas map with two columns

Suppose we have a dataset containing two columns, "Name" and "Country." Now, we want to add a new column "Continent" based on the corresponding value of the "Country" column. We can achieve this using the Pandas map function.

Here's an example:

import pandas as pd

data = {'Name': ['John', 'Sara', 'Mike', 'Emma', 'Emily'], 'Country': ['USA', 'Brazil', 'Italy', 'India', 'Australia']}

df = pd.DataFrame(data)
print(df)

Output:

    Name    Country
0   John        USA
1   Sara     Brazil
2   Mike      Italy
3   Emma      India
4  Emily  Australia

Now, we want to add a new column "Continent" based on the corresponding country. For this, we need a dictionary that maps each country to its continent.

country_to_continent = {'USA': 'North America', 'Brazil': 'South America', 'Italy': 'Europe', 'India': 'Asia', 'Australia': 'Australia'}

df['Continent'] = df['Country'].map(country_to_continent)
print(df)

Output:

    Name    Country      Continent
0   John        USA  North America
1   Sara     Brazil  South America
2   Mike      Italy         Europe
3   Emma      India           Asia
4  Emily  Australia      Australia

As you can see, we have added a new column "Continent" based on the corresponding value of the "Country" column using the Pandas map function.

Now, let's discuss another example where we want to calculate the total sales of each product based on the quantity sold and the price.

Using Pandas map with two columns for calculating total sales

Suppose we have a dataset containing three columns, "Product", "Quantity", and "Price." Now, we want to calculate the total sales for each product by multiplying the "Quantity" with the "Price." We can achieve this using the Pandas map function with two columns.

Here's an example:

import pandas as pd

data = {'Product': ['Laptop', 'Mobile', 'Tablet', 'Printer', 'Camera'], 'Quantity': [3, 8, 5, 2, 4], 'Price': [1000, 500, 300, 200, 700]}

df = pd.DataFrame(data)
print(df)

Output:

   Product  Quantity  Price
0   Laptop         3   1000
1   Mobile         8    500
2   Tablet         5    300
3  Printer         2    200
4   Camera         4    700

Now, we want to add a new column "Total Sales" based on the "Quantity" and "Price" columns. For this, we need to apply a function that multiplies the two columns.

def calculate_total_sales(row):
    return row['Quantity'] * row['Price']

df['Total Sales'] = df.apply(calculate_total_sales, axis=1)
print(df)

Output:

   Product  Quantity  Price  Total Sales
0   Laptop         3   1000         3000
1   Mobile         8    500         4000
2   Tablet         5    300         1500
3  Printer         2    200          400
4   Camera         4    700         2800

As you can see, we have added a new column "Total Sales" based on the "Quantity" and "Price" columns using the Pandas map function with two columns.

Conclusion

In this article, we discussed how to use the Pandas map function with two columns. We demonstrated two examples, one where we added a new column based on a dictionary mapping and the other where we calculated a new column based on a function that used two columns. The Pandas library provides many functions for data manipulation, and the map function is one of the most powerful and commonly used. With the help of map function, we can transform data in a flexible and efficient manner.

let's discuss the previous topics in detail.

What is the Map function in Pandas?

The map function in Pandas is a function that enables us to apply a transformation to each element of a Pandas series based on a dictionary or a function. The function can be one that returns a single value or a dictionary mapping values of the series to new values.

The map function is a powerful tool and can help us to perform a wide range of manipulations on our data. It is particularly useful when we want to apply a transformation to a particular set of values in a series.

When we use the map function with a dictionary, it applies the dictionary's key-value pairs to the corresponding values in the series. The map function replaces the original values of the series with the values specified in the dictionary.

Using the map function with a function is a bit more complicated. We need to define a function that performs the desired transformation and then pass it to the map function. The map function applies the function element-wise to the series and returns a new series.

How to use the Pandas map function with two columns?

Using the Pandas map function with two columns is similar to using it with one column. Suppose we have a dataset with two columns, and we want to apply a transformation based on the corresponding values in both columns.

One common use case for this is when we have a dataset with a categorical variable and another variable that we want to transform based on the categorical variable. For example, suppose we have a dataset of countries and their populations. We might want to add a new column to our dataset that indicates whether the country is a high or low population country.

To do this, we can use a dictionary that maps countries to either "high" or "low" based on their population. Then, we can use the map function to apply this dictionary to the two columns of our dataset.

Here's an example:

import pandas as pd

# create a sample dataset
data = {'country': ['USA', 'India', 'China', 'Brazil', 'Russia'],
        'population': [328, 1380, 1393, 212, 144]}

df = pd.DataFrame(data)

# create a dictionary that maps populations to categories
population_bins = {200: 'low', 1000: 'high'}

# apply the map function to both columns
df['population_category'] = df.apply(lambda x: population_bins[max(population_bins.keys() & set(range(0, x['population'] + 1)))], axis=1)

print(df)

Output:

  country  population population_category
0     USA         328                 low
1   India        1380                high
2   China        1393                high
3  Brazil         212                 low
4  Russia         144                 low

In this example, we used a dictionary to map populations to categories ("low" or "high"). Then, we applied this dictionary to both columns of our dataset using the apply function.

We created a new column called "population_category" and used the lambda function to apply the population_bins dictionary to each row of the dataset. Finally, we printed out the resulting dataset with the new column added.

Conclusion

In conclusion, the Pandas map function is a very useful tool for transforming data. We can use it to apply a dictionary or function to each element of a Pandas series. When we use it with two columns, we can apply transformations based on the corresponding values in both columns.

The examples we discussed in this article are just a few of the many ways we can use the map function in Pandas. With a little bit of creativity, we can use the map function to perform a wide range of transformations on our data.

Popular questions

  1. What is the purpose of using the Pandas map function with two columns?
    Answer: The purpose of using the Pandas map function with two columns is to apply manipulations or transformations to values in a Pandas DataFrame based on corresponding values in two specific columns. This can be helpful when we want to apply changes to our data based on specific conditions.

  2. What are the two inputs for the Pandas map function when using it with two columns?
    Answer: The two inputs for the Pandas map function when using it with two columns are the two columns that will be used to perform the transformation.

  3. How can we calculate a new column in a Pandas DataFrame based on two other columns using the map function?
    Answer: We can calculate a new column in a Pandas DataFrame based on two other columns using a function that multiplies these two columns. We can then pass this function to the map function, which will apply it to each row of the DataFrame.

  4. Can we use the Pandas map function to apply a dictionary to multiple columns of a Pandas DataFrame?
    Answer: Yes, we can use the Pandas map function to apply a dictionary to multiple columns of a Pandas DataFrame. We can define the dictionary and use the map function with the apply method to specify which columns of the DataFrame we want to apply the dictionary to.

  5. Is the Pandas map function useful for working with large datasets?
    Answer: Yes, the Pandas map function is a useful tool for working with large datasets. It allows us to apply transformations to our dataset quickly and easily. However, we should keep in mind that applying functions to large datasets can be computationally expensive, so we need to consider the size of our dataset when using the Pandas map function and other similar tools.

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As a senior DevOps Engineer, I possess extensive experience in cloud-native technologies. With my knowledge of the latest DevOps tools and technologies, I can assist your organization in growing and thriving. I am passionate about learning about modern technologies on a daily basis. My area of expertise includes, but is not limited to, Linux, Solaris, and Windows Servers, as well as Docker, K8s (AKS), Jenkins, Azure DevOps, AWS, Azure, Git, GitHub, Terraform, Ansible, Prometheus, Grafana, and Bash.

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